For a while now, using traditional CMS has been the best way to go for most companies and individuals. But the flexibility of these has been limiting and they have been difficult to scale. This has led companies to embrace the idea of headless CMS and the flexibility and scalability that comes with it.
The start of 2020 was not what most of us pictured it would be. And while the world is going through changes, there have been a few revelations on some gross inadequacy in the healthcare sector. But as developers this brings forth a new challenge to overcome. One of the ways to relieve and assist the Healthcare industry is by providing viable solutions. The creation of a telemedical application is just such a solution.
LMS stands for Learning Management System and in simple terms, it is a suite of tools that work together to provide assessment, tracking, documentation, reporting and a host of other functions for a digital learning environment. It is a means to get the full educational experience while not being physically present. Or in the case of institutions and educators, it is a platform by which they can train and educate students or learners outside a physical gathering.
A serverless cloud computing execution model is one where the cloud provider dynamically manages the provision and allocation of servers. When you want to build an app, your development structure is broken down into two major parts. The first part includes general expectations for the running of the app, this is what AWS calls the “undifferentiated heavy lifting” generally found in every app and usually common from one to the other and includes things like setting up and running the servers where you deploy the app or running your CD tools.
Redux Toolkit popularity is growing every month. What exactly helps developers to write code faster, easier, more clearly? One of the helpers is `createSlice` function. `createSlice` takes an object of reducer functions, a slice name, and an initial state value and lets us auto-generate action types and action creators, based on the names of the reducer functions that we supply. It also helps you organize all of your Redux-related logic for a given slice into a single file.
Today we would like to switch gears a bit and get our feet wet with another BigData combo of Python and Impala. The reason for this is because there are some limitations that exist when using Hive that might prove a deal-breaker for your specific solution. Impala might be a better route to take instead.